DESCRIPTION (Applicant's abstract): The long-term objective of the proposed work is to understand signal processing as it applies to homeodynamic control of respiratory and related autonomic functions. The work focuses primarily on processing of one particular afferent signal, that of the pulmonary stretch receptor. Activation of these mechanoreceptors induces a host of motor control responses including alteration of respiratory effort, cardiac rate and output, and bronchodilatation. Clinically, alteration of the function of these and other receptors by pathophysiological conditions (e.g. pulmonary edema, congestive heart failure) may contribute to worsening of these disease states. On a more basic level, the processing of these input signals by central nervous system networks remains unclear even in the healthy state. For example, it is not known how (or what types of) information regarding the peripheral physiological signal (pulmonary distention) is encoded by single, or groups of, neurons within the central nervous system. What is known is that the nervous system analyzes this and other signals continuously in order to control the relevant functions, and that the currency of information exchange among and between neurons is their ongoing spike trains. In order to address these issues, we have developed and propose to apply a strict probabilistic measure of neural encoding and decoding schemes that quantify exactly what and how much information is transmitted by individual, defined elements in the control circuits. This is to be done by performing in vivo recordings of individual or small populations of neurons throughout the system, all while exposing the system to continuous stimulus perturbations. The subsequent analysis will systematically define the quantity and quality of pulmonary distension information present in the (1) primary afferent neurons, (2) the dorsal respiratory group neurons within the NTS, (3) the ventral respiratory group, and (4) the motor neuron population. In addition, we will determine the properties and mechanisms behind information transfer between neurons in this system, including integration and learning. The results of these studies should significantly clarify the mechanisms underlying the control strategies in this system, and contribute a base for diagnostic and therapeutic approaches to clinical treatment of pathologies thereof.